Journal of complex networks最新文献_第7页

Community detection and reciprocity in networks by jointly modelling pairs of edges 基于边对联合建模的网络社区检测与互易性研究

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac034

Martina Contisciani;Hadiseh Safdari;Caterina De Bacco

{"title":"Community detection and reciprocity in networks by jointly modelling pairs of edges","authors":"Martina Contisciani;Hadiseh Safdari;Caterina De Bacco","doi":"10.1093/comnet/cnac034","DOIUrl":"https://doi.org/10.1093/comnet/cnac034","url":null,"abstract":"To unravel the driving patterns of networks, the most popular models rely on community detection algorithms. However, these approaches are generally unable to reproduce the structural features of the network. Therefore, attempts are always made to develop models that incorporate these network properties beside the community structure. In this article, we present a probabilistic generative model and an efficient algorithm to both perform community detection and capture reciprocity in networks. Our approach jointly models pairs of edges with exact two-edge joint distributions. In addition, it provides closed-form analytical expressions for both marginal and conditional distributions. We validate our model on synthetic data in recovering communities, edge prediction tasks and generating synthetic networks that replicate the reciprocity values observed in real networks. We also highlight these findings on two real datasets that are relevant for social scientists and behavioural ecologists. Our method overcomes the limitations of both standard algorithms and recent models that incorporate reciprocity through a pseudo-likelihood approximation. The inference of the model parameters is implemented by the efficient and scalable expectation–maximization algorithm, as it exploits the sparsity of the dataset. We provide an open-source implementation of the code online.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"10 4","pages":"1121-1122"},"PeriodicalIF":2.1,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8016804/10070447/10070458.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49943942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Investigating cognitive ability using action-based models of structural brain networks 利用基于行为的脑结构网络模型研究认知能力

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac037

Viplove Arora;Enrico Amico;Joaquín Goñi;Mario Ventresca

{"title":"Investigating cognitive ability using action-based models of structural brain networks","authors":"Viplove Arora;Enrico Amico;Joaquín Goñi;Mario Ventresca","doi":"10.1093/comnet/cnac037","DOIUrl":"https://doi.org/10.1093/comnet/cnac037","url":null,"abstract":"Recent developments in network neuroscience have highlighted the importance of developing techniques for analysing and modelling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use wiring rules to synthesize networks closely resembling the topology of a given connectome. Successful models can highlight the principles by which a network is organized (identify structural features that arise from wiring rules versus those that emerge) and potentially uncover the mechanisms by which it grows and develops. Previous research has shown that such models can validate the effectiveness of spatial embedding and other (non-spatial) wiring rules in shaping the network topology of the human connectome. In this research, we propose variants of the action-based model that combine a variety of generative factors capable of explaining the topology of the human connectome. We test the descriptive validity of our models by evaluating their ability to explain between-subject variability. Our analysis provides evidence that geometric constraints are vital for connectivity between brain regions, and an action-based model relying on both topological and geometric properties can account for between-subject variability in structural network properties. Further, we test correlations between parameters of subject-optimized models and various measures of cognitive ability and find that higher cognitive ability is associated with an individual's tendency to form long-range or non-local connections.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"10 4","pages":"0245-0251"},"PeriodicalIF":2.1,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49943939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online dynamic rumour propagation model considering punishment mechanism and individual personality characteristics 考虑惩罚机制和个体人格特征的在线动态谣言传播模型

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac038

Chengai Sun;Donghang Qiao;Liqing Qiu

{"title":"Online dynamic rumour propagation model considering punishment mechanism and individual personality characteristics","authors":"Chengai Sun;Donghang Qiao;Liqing Qiu","doi":"10.1093/comnet/cnac038","DOIUrl":"https://doi.org/10.1093/comnet/cnac038","url":null,"abstract":"In the Internet era, rumours will spread rapidly in the network and hinder the development of all aspects of society. To create a harmonious network environment, it is essential to take punitive measures against malicious rumour mongers on social platforms. Take the measure of forbidden as an example. The forbidden one may stop spreading rumours because of being punished, or he may become a disseminator again because of paranoia. Other people who know rumours may become alert and stop propagating rumours or temporarily forget rumours. And therefore, the forbidden state is added to describe the above phenomenon, and the SIFR (Ignorant–Disseminator–Forbidden–Restorer) model is proposed. Taking the vigilance and paranoia derived from punishment measures into account, the connection edges from the forbidden to the disseminator and from the disseminator to the restorer are increased in this model. And then, the stability of SIFR model is proved by using the basic regeneration number and Routh–Hurwitz stability theorem. The simulation results demonstrate that individual paranoia may do harm to the control of rumour dissemination. While the punishment mechanism, individual forgetting mechanism and vigilance can effectively curb the spread of rumours.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"10 4","pages":"22-37"},"PeriodicalIF":2.1,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49943397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Universal behaviour of the growth method and importance of local hubs in cascading failure 增长方法的普遍行为及局部枢纽在级联失效中的重要性

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac028

Wonhee Jeong;Unjong Yu

引用次数: 0

Centrality measures in interval-weighted networks 区间加权网络中的中心性测度

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac031

Hélder Alves;Paula Brito;Pedro Campos

引用次数: 3

Data fusion reconstruction of spatially embedded complex networks 空间嵌入式复杂网络的数据融合重建

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac032

Jie Sun;Fernando J Quevedo;Erik M Bollt

引用次数: 1

Analysing region of attraction of load balancing on complex network 复杂网络负载均衡的吸引域分析

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac025

Mengbang Zou;Weisi Guo

引用次数: 2

Motif importance measurement based on multi-attribute decision 基于多属性决策的主题重要性度量

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac023

Biao Feng;Yunyun Yang;Liao Zhang;Shuhong Xue;Xinlin Xie;Jiianrong Wang;Gang Xie

引用次数: 0

Reconstruction of cascading failures in dynamical models of power grids 电网动力学模型中级联故障的重构

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac035

Alessandra Corso;Lucia Valentina Gambuzza;Federico Malizia;Giovanni Russo;Vito Latora;Mattia Frasca

引用次数: 1

Evaluating node embeddings of complex networks 评估复杂网络的节点嵌入

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-07-01 DOI: 10.1093/comnet/cnac030

Arash Dehghan-Kooshkghazi;Bogumił Kamiński;Łukasz Kraiński;Paweł Prałat;François Théberge;Ali Pinar

{"title":"Evaluating node embeddings of complex networks","authors":"Arash Dehghan-Kooshkghazi;Bogumił Kamiński;Łukasz Kraiński;Paweł Prałat;François Théberge;Ali Pinar","doi":"10.1093/comnet/cnac030","DOIUrl":"https://doi.org/10.1093/comnet/cnac030","url":null,"abstract":"Graph embedding is a transformation of nodes of a graph into a set of vectors. A good embedding should capture the graph topology, node-to-node relationship and other relevant information about the graph, its subgraphs and nodes. If these objectives are achieved, an embedding is a meaningful, understandable, compressed representations of a network that can be used for other machine learning tools such as node classification, community detection or link prediction. In this article, we do a series of extensive experiments with selected graph embedding algorithms, both on real-world networks as well as artificially generated ones. Based on those experiments, we formulate the following general conclusions. First, we confirm the main problem of node embeddings that is rather well-known to practitioners but less documented in the literature. There exist many algorithms available to choose from which use different techniques and have various parameters that may be tuned, the dimension being one of them. One needs to ensure that embeddings describe the properties of the underlying graphs well but, as our experiments confirm, it highly depends on properties of the network at hand and the given application in mind. As a result, selecting the best embedding is a challenging task and very often requires domain experts. Since investigating embeddings in a supervised manner is computationally expensive, there is a need for an unsupervised tool that is able to select a handful of promising embeddings for future (supervised) investigation. A general framework, introduced recently in the literature and easily available on GitHub repository, provides one of the very first tools for an unsupervised graph embedding comparison by assigning the ‘divergence score’ to embeddings with a goal of distinguishing good from bad ones. We show that the divergence score strongly correlates with the quality of embeddings by investigating three main applications of node embeddings: node classification, community detection and link prediction.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"10 4","pages":"56001-1098"},"PeriodicalIF":2.1,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49943946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11